Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "49" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 22 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 22 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459998 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.422306 | -0.046688 | -0.496143 | 0.160671 | -0.331791 | -0.178620 | 0.146355 | -0.093157 | 0.5302 | 0.5716 | 0.3723 | nan | nan |
| 2459997 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.454403 | -0.557696 | 0.063950 | -0.250453 | -0.296478 | -0.544159 | 0.226013 | 0.053987 | 0.5602 | 0.6024 | 0.3839 | nan | nan |
| 2459996 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.460398 | -0.350218 | 0.544301 | -0.321857 | -0.619483 | -0.685183 | 0.007637 | 0.798637 | 0.5603 | 0.5998 | 0.3950 | nan | nan |
| 2459995 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.311008 | -0.129490 | -0.757803 | 0.069525 | 0.020956 | 0.116735 | 0.024353 | 2.423420 | 0.5609 | 0.5978 | 0.3793 | nan | nan |
| 2459994 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.265544 | -0.293863 | -0.689296 | -0.179737 | 0.053643 | -0.708227 | -0.297925 | 3.657036 | 0.5506 | 0.5887 | 0.3736 | nan | nan |
| 2459993 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.289347 | -0.006640 | -0.978049 | 0.172533 | 1.627018 | -0.105986 | -0.229886 | 1.317910 | 0.5442 | 0.6043 | 0.3898 | nan | nan |
| 2459991 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.215736 | -0.408398 | -0.148923 | -0.236382 | 0.876459 | -0.756130 | -0.053814 | 0.132528 | 0.5486 | 0.5854 | 0.3835 | nan | nan |
| 2459990 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.193560 | -0.238356 | -0.164799 | -0.289459 | 0.896402 | -0.667283 | 0.205669 | 5.265605 | 0.5459 | 0.5863 | 0.3813 | nan | nan |
| 2459989 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.056009 | 0.010566 | -0.767541 | 0.195251 | 0.687493 | -0.588985 | -0.090604 | 3.914892 | 0.5447 | 0.5857 | 0.3833 | nan | nan |
| 2459988 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.117269 | -0.256660 | -0.244080 | -0.301434 | 1.281810 | -0.478502 | 0.650360 | 4.729693 | 0.5333 | 0.5735 | 0.3676 | nan | nan |
| 2459987 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.298655 | -0.132487 | -0.799150 | 0.166178 | 0.195524 | -0.178204 | 1.682981 | 1.924559 | 0.5564 | 0.5962 | 0.3741 | nan | nan |
| 2459986 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.348808 | 0.013578 | -0.874606 | -0.067584 | 1.064277 | 1.532325 | 1.200154 | 2.647379 | 0.5672 | 0.6063 | 0.3324 | nan | nan |
| 2459985 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.290391 | -0.064499 | -0.768315 | 0.028685 | -0.321114 | -0.597569 | 0.376592 | 1.306285 | 0.5487 | 0.5871 | 0.3770 | nan | nan |
| 2459984 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.986794 | -0.297783 | -1.308679 | 0.153845 | 0.508198 | 0.440683 | 1.464603 | 1.991001 | 0.5688 | 0.5980 | 0.3496 | nan | nan |
| 2459983 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.132854 | -0.209521 | -0.799897 | 0.337741 | 1.151072 | -0.397235 | 0.326471 | -0.592560 | 0.5904 | 0.6384 | 0.3107 | nan | nan |
| 2459982 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.049531 | -0.621304 | 0.085376 | -0.865348 | -0.081640 | -0.954800 | 1.047335 | 0.651746 | 0.6233 | 0.6516 | 0.3004 | nan | nan |
| 2459981 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.095531 | -0.091508 | -1.038146 | 0.581713 | 1.208225 | -0.315766 | 0.074871 | 0.095916 | 0.5426 | 0.5893 | 0.3807 | nan | nan |
| 2459980 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.077851 | -0.587687 | -0.362547 | -0.898825 | 0.624877 | 1.315494 | 0.987796 | 1.333711 | 0.5984 | 0.6329 | 0.3149 | nan | nan |
| 2459979 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.216289 | -0.332584 | -1.125536 | -0.510577 | 0.847097 | -0.848817 | 0.753817 | 8.394044 | 0.5385 | 0.5838 | 0.3782 | nan | nan |
| 2459978 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.005198 | -0.342304 | -1.156313 | -0.318773 | 0.941972 | -0.820633 | 1.244240 | 11.356959 | 0.5363 | 0.5785 | 0.3852 | nan | nan |
| 2459977 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.225287 | -0.588956 | -0.318797 | -1.007908 | 1.317686 | -0.233969 | 2.990702 | 16.942456 | 0.5075 | 0.5501 | 0.3455 | nan | nan |
| 2459976 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.117825 | -0.429724 | -0.393018 | -0.982830 | 0.758836 | -1.171126 | -0.038478 | 9.894392 | 0.5482 | 0.5901 | 0.3802 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 49 | N06 | not_connected | nn Power | 0.160671 | -0.422306 | -0.046688 | -0.496143 | 0.160671 | -0.331791 | -0.178620 | 0.146355 | -0.093157 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 49 | N06 | not_connected | ee Temporal Discontinuties | 0.226013 | -0.454403 | -0.557696 | 0.063950 | -0.250453 | -0.296478 | -0.544159 | 0.226013 | 0.053987 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 49 | N06 | not_connected | nn Temporal Discontinuties | 0.798637 | -0.460398 | -0.350218 | 0.544301 | -0.321857 | -0.619483 | -0.685183 | 0.007637 | 0.798637 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 49 | N06 | not_connected | nn Temporal Discontinuties | 2.423420 | -0.311008 | -0.129490 | -0.757803 | 0.069525 | 0.020956 | 0.116735 | 0.024353 | 2.423420 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 49 | N06 | not_connected | nn Temporal Discontinuties | 3.657036 | -0.265544 | -0.293863 | -0.689296 | -0.179737 | 0.053643 | -0.708227 | -0.297925 | 3.657036 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 49 | N06 | not_connected | ee Temporal Variability | 1.627018 | 0.289347 | -0.006640 | -0.978049 | 0.172533 | 1.627018 | -0.105986 | -0.229886 | 1.317910 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 49 | N06 | not_connected | ee Temporal Variability | 0.876459 | 0.215736 | -0.408398 | -0.148923 | -0.236382 | 0.876459 | -0.756130 | -0.053814 | 0.132528 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 49 | N06 | not_connected | nn Temporal Discontinuties | 5.265605 | -0.238356 | 0.193560 | -0.289459 | -0.164799 | -0.667283 | 0.896402 | 5.265605 | 0.205669 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 49 | N06 | not_connected | nn Temporal Discontinuties | 3.914892 | 0.010566 | 0.056009 | 0.195251 | -0.767541 | -0.588985 | 0.687493 | 3.914892 | -0.090604 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 49 | N06 | not_connected | nn Temporal Discontinuties | 4.729693 | -0.256660 | 0.117269 | -0.301434 | -0.244080 | -0.478502 | 1.281810 | 4.729693 | 0.650360 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 49 | N06 | not_connected | nn Temporal Discontinuties | 1.924559 | -0.298655 | -0.132487 | -0.799150 | 0.166178 | 0.195524 | -0.178204 | 1.682981 | 1.924559 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 49 | N06 | not_connected | nn Temporal Discontinuties | 2.647379 | 0.013578 | 0.348808 | -0.067584 | -0.874606 | 1.532325 | 1.064277 | 2.647379 | 1.200154 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 49 | N06 | not_connected | nn Temporal Discontinuties | 1.306285 | -0.064499 | -0.290391 | 0.028685 | -0.768315 | -0.597569 | -0.321114 | 1.306285 | 0.376592 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 49 | N06 | not_connected | nn Temporal Discontinuties | 1.991001 | -0.986794 | -0.297783 | -1.308679 | 0.153845 | 0.508198 | 0.440683 | 1.464603 | 1.991001 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 49 | N06 | not_connected | ee Temporal Variability | 1.151072 | 0.132854 | -0.209521 | -0.799897 | 0.337741 | 1.151072 | -0.397235 | 0.326471 | -0.592560 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 49 | N06 | not_connected | ee Temporal Discontinuties | 1.047335 | 0.049531 | -0.621304 | 0.085376 | -0.865348 | -0.081640 | -0.954800 | 1.047335 | 0.651746 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 49 | N06 | not_connected | ee Temporal Variability | 1.208225 | -0.091508 | 0.095531 | 0.581713 | -1.038146 | -0.315766 | 1.208225 | 0.095916 | 0.074871 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 49 | N06 | not_connected | nn Temporal Discontinuties | 1.333711 | -0.587687 | -0.077851 | -0.898825 | -0.362547 | 1.315494 | 0.624877 | 1.333711 | 0.987796 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 49 | N06 | not_connected | nn Temporal Discontinuties | 8.394044 | 0.216289 | -0.332584 | -1.125536 | -0.510577 | 0.847097 | -0.848817 | 0.753817 | 8.394044 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 49 | N06 | not_connected | nn Temporal Discontinuties | 11.356959 | -0.342304 | 0.005198 | -0.318773 | -1.156313 | -0.820633 | 0.941972 | 11.356959 | 1.244240 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 49 | N06 | not_connected | nn Temporal Discontinuties | 16.942456 | 0.225287 | -0.588956 | -0.318797 | -1.007908 | 1.317686 | -0.233969 | 2.990702 | 16.942456 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 49 | N06 | not_connected | nn Temporal Discontinuties | 9.894392 | -0.429724 | 0.117825 | -0.982830 | -0.393018 | -1.171126 | 0.758836 | 9.894392 | -0.038478 |